Abstract
Technology convergence has been the subject of many prior studies, yet most have focussed on the structural patterns of convergence between a pair of technologies rather than the dynamic aspects of multi-technology convergence. This study proposes a machine learning approach to anticipating multi-technology convergence using patent information. For this, a patent database is first constructed using the United States Patent and Trademark Office database, distinguishing the primary class from other patent classes to consider the direction of multi-technology convergence. Second, association rule mining is employed to construct technology ecology networks describing the significant structural patterns of multi-technology convergence for different time periods in the form of a primary patent class → supplementary patent classes. Third, the technology ecology networks between the periods are compared to identify implications on the changing patterns of multi-technology convergence. Finally, link prediction analysis based on logistic regression models is utilised to provide insight into the prospects of multi-technology convergence by identifying the links to be added to or removed from the network. Based on this, we also discuss the characteristics of the proposed approach and the technological impact and uncertainty of the identified patterns of multi-technology convergence. The case of drug, bio-affecting, and body treating compositions technology is presented herein.
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Notes
According to the United States Patent and Trademark Office, every US patent has one and only one primary class (i.e. first bold class) that represents the main idea of invention described in the patent. It is double-vetted and reliable since the primary class is used for routing the application along the patent office. If there is a mistake in primary classification, the examiner will reject the patent, and it will be reclassified and routed to a different examiner (http://www.acclaimip.com/the-us-patent-classification-system-class-types/).
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Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIP) (No. 2019R1A6A3A13096839) and and the Sogang University Research Grant of 2020 (No. 202010009.01).
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Appendices
Appendix 1: An example of constructing a technology ecology network
We present a step-by-step example of constructing a technology ecology network describing the significant structural patterns of multi-technology convergence. Five patents are used in this example, as shown in Table 8, and the threshold values for CP and CI are set to 0.2 and 0.5.
First, we identify frequently co-occurred structural patterns based on the Apriori algorithm. Specifically, the frequent individual primary and supplementary classes (i.e. one-item set) that exceed the prescribed threshold values for CP (i.e. 0.2) are identified; the frequent patterns with one primary class and one supplementary class (i.e. two-item set) are identified from the one-item set; the two-item set is extended to the three-item set with one primary class and two supplementary classes by adding one supplementary class at a time. This extension process terminates when no further extensions are found. Table 9 shows the results of Apriori algorithm employed in this study, where the frequently co-occurred structural patterns derived from the five patents are highlighted in bold.
Next, we compute the values of CI and CS for the frequently co-occurred patterns, as shown in Table 10. Three patterns are identified as significant as their CI and CS values are greater than the threshold value for CI (i.e. 0.5) and one, respectively.
Finally, the significant structural patterns are represented as the technology ecology network. In Fig. 5, a source and a target node are a primary class and supplementary classes; the size of a node represents the number of the corresponding pattern’s occurrence; a link represents the CI among the associated classes. White and grey nodes denote single and multiple patent classes; blue and red links indicate within-technology convergence where the primary patent class also appears in the supplementary patent class part and between-technology convergence where the primary patent class is not included in the patent class segment, respectively.
Appendix 2
See Table 11.
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Lee, C., Hong, S. & Kim, J. Anticipating multi-technology convergence: a machine learning approach using patent information. Scientometrics 126, 1867–1896 (2021). https://doi.org/10.1007/s11192-020-03842-6
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DOI: https://doi.org/10.1007/s11192-020-03842-6